February 16, 2016
Charles J. Geyer
Professor
School of Statistics
University of Minnesota
313 Ford Hall
224 Church St. S. E.
Minneapolis, MN 55455
(612) 625-8511
charlie@stat.umn.edu
Degrees:
BS 1972 (physics), Hampden-Sydney College
MS 1988 (statistics), PhD 1990 (statistics), University of Washington
Academic Experience:
University of Washington, Department of Statistics. Teaching Assistant,
Research Assistant, and Instructor, 1986-1990.
University of Chicago, Department of Statistics. NSF Postdoctoral Fellow,
1990-1991.
University of Minnesota, School of Statistics. Assistant Professor, 19901995. Associate Professor, 1995-2000. Professor, since 2000.
University of Minnesota, Minnesota Center for Philosophy of Science. Resident Fellow, since 2009.
Thesis Advisor: Elizabeth A. Thompson
Honors:
The paper Shaw, Geyer, Wagenius, Hangelbroek, and Etterson (2008)
has been given the 2009 Presidential Award of the American Society of
Naturalists. This award is for the best paper published in The American Naturalist during the calendar year preceding the President’s term of
office.
Pn. D. Thesis:
Geyer, C. J. (1990). Likelihood and Exponential Families. Ph. D. Thesis,
Department of Statistics, University of Washington. http://purl.umn.
edu/56330.
Publications:
Geyer, C. J. and Thompson, E. A. (1988). Gene survival in the Asian wild
horse (Equus przewalskii ): I. Dependence of gene survival in the Calgary
breeding group pedigree. Zoo Biology, 7, 313–327.
Geyer, C. J., Thompson, E. A. and Ryder, O. A. (1989). Gene survival
in the Asian wild horse (Equus przewalskii ) II. Gene survival in the whole
population, in subgroups, and through history. Zoo Biology 8, 313–329.
Geyer, C. J. (1991). Constrained maximum likelihood exemplified by isotonic convex logistic regression. J. Amer. Statist. Assoc., 86, 717–724.
Geyer, C. J. and Thompson, E. A. (1992). Constrained Monte Carlo maximum likelihood for dependent data, (with discussion). J. Roy. Statist. Soc.
Ser. B, 54 657–699.
Lin, D. Y. and Geyer C. J. (1992). Computational methods for semiparametric linear regression with censored data. J. Comput. Graph. Statist.,
1 77–90.
Geyer, C. J. (1992). Practical Markov chain Monte Carlo (with discussion). Statist. Sci., 7 473–511.
Geyer, C. J. (1993). Discussion on the meeting on the Gibbs Sampler and
other Monte Carlo methods. J. Roy. Statist. Soc. Ser. B, 55 74–75.
Geyer, C. J., Ryder, O. A., Chemnick, L. G. and Thompson, E. A. (1993).
Analysis of relatedness in the California condors from DNA fingerprints.
Molecular Biology and Evolution, 10 571–589.
Geyer, C. J. (1994). On the convergence of Monte Carlo maximum likelihood calculations. J. Roy. Statist. Soc. Ser. B, 56 261–274.
Newton, M. A. and Geyer, C. J. (1994). Bootstrap recycling: A Monte
Carlo algorithm for the nested bootstrap. J. Amer. Statist. Assoc., 89
905–912.
Gentleman, R. and Geyer, C. J. (1994). Maximum likelihood for intervalcensored data: Computation and consistency. Biometrika, 81 618–623.
Geyer, C. J. and Møller, J. (1994). Simulation procedures and likelihood
inference for spatial point processes. Scand. J. Statist., 21 359–373.
Geyer, C. J. (1994). On the Asymptotics of Constrained M-Estimation.
Ann. Statist., 22 1993–2010.
Chan, K. S. and Geyer, C. J. (1994). Discussion of the paper by Tierney.
Ann. Statist., 22 1747–1758.
Geyer, C. J. (1995). Conditioning in Markov chain Monte Carlo. J.
Comput. Graph. Statist., 4 2031–2050.
Geyer, C. J. and Thompson, E. A. (1995). Annealing Markov chain Monte
Carlo with applications to ancestral inference. J. Amer. Statist. Assoc.,
90 909–920.
Geyer, C. J. (1995). Discussion of the paper “Bayesian Computation and
Stochastic Systems” by Julian Besag, Peter Green, David Higdon and
Kerrie Mengersen. Statist. Sci., 10 46–48.
Geyer, C. J. and Tierney, L. (1995). On the convergence of Monte Carlo
approximations to the posterior density. In Bayesian Statistics and Econometrics: Essays in Honor of Arnold Zellner, eds. D. Berry, K. M. Chaloner,
and J. K. Geweke, New York: Wiley, 389–396.
Geyer, C. J. (1996). Estimation and Optimization of Functions. In Markov
Chain Monte Carlo in Practice, eds. W. R. Gilks, S. Richardson, and D. J.
Spiegelhalter, London: Chapman and Hall, 241–258.
Geyer, C. J. (1996). Discussion of the paper by Dr. Walley. J. Roy. Statist.
Soc. Ser. B, 58 40–41.
Valberg, S. J., Geyer, C., Sorum, S. A., and Cardinet, G. H., III. (1996).
Familial Incidence of Exertional Rhabdomyolysis in Quarter Horse-related
breeds. American Journal of Veterinary Research 57 286–290.
Shaw, F. H. and Geyer, C. J. (1997). Estimation and testing in constrained
covariance component models. Biometrika 84 95–102.
Meeden, G., Geyer, C., Lang, J. and Funo, E. (1998). The admissibility of
the maximum likelihood estimator for decomposable log-linear interaction
models for contingency tables. Communications in Statistics–Theory and
Methods 27 473–494.
Hobert, J. P. and Geyer, C. J. (1998). Geometric ergodicity of Gibbs and
block Gibbs samplers for a hierarchical random effects model. Journal of
Multivariate Analysis 67 414–430.
Geyer, C. J. (1999). Likelihood Inference for Spatial Point Processes. In
Stochastic Geometry: Likelihood and Computation, eds. W. Kendall, O.
Barndorff-Nielsen and M. N. M. van Lieshout, London: Chapman and
Hall/CRC, 141–172.
Chen, L. S., Geisser, S. and Geyer, C. J. (1999). Monte Carlo Minimization for One-Step Ahead Sequential Control. In Diagnosis and Prediction,
IMA Vol. 114, 109–129.
Shaw, F. H., Promislow, D. E. L., Tatar, M., Hughes, K. A. and Geyer,
C. J. (1999). Towards reconciling inferences concerning genetic variation
in senescence. Genetics 152 553–566.
Pletcher, S. D. and Geyer, C. J. (1999). The genetic analysis of agedependent traits: modeling the character process. Genetics 153 825–835.
MacLeay, J. M., Valberg, S. J., Geyer, C. J., Sorum, S. A., and Sorum,
M. D. (1999). Heritable basis for recurrent exertional rhabdomyolysis in
thoroughbred racehorses. American Journal of Veterinary Research 60
250–256.
Shaw, F. H., Geyer, C. J., and Shaw, R. G. (2002). A Comprehensive Model of Mutations Affecting Fitness and Inferences for Arabidopsis
thaliana Evolution 56 453–463.
Shaw, R. G., Shaw, F. H., and Geyer, C. J. (2003). What fraction of
mutations reduce fitness: A reply to Keightley and Lynch. Evolution 57
686–689.
Geyer, C. J. and Meeden, G. D. (2005). Fuzzy and Randomized Confidence Intervals and P-values (with discussion). Statistical Science 20
358–387.
Geyer, C. J. (2005). Discussion on the paper by Baddeley et al. J. Roy.
Statist. Soc. Ser. B, 67 660.
Sung, Y. J. and Geyer, C. J. (2007). Monte Carlo Likelihood Inference
for Missing Data Models. http://www.stat.umn.edu/geyer/bernor/.
Annals of Statistics 35 990–1011.
Thompson, E. A. and Geyer, C. J. (2007). Fuzzy P-values in Latent
Variable Problems. Biometrika 94 49–60.
Geyer, C. J., Wagenius, S., and Shaw, R. G. (2007). Aster Models for Life
History Analysis. http://www.stat.umn.edu/geyer/aster/. Biometrika
94 415–426.
Shaw, R. G., Geyer, C. J., Wagenius, S., Hangelbroek, H. H., and Etterson,
J. R. (2008). Unifying Life History Analysis for Inference of Fitness and
Population Growth. American Naturalist 172 E35–E47. (e-paper http:
//www.journals.uchicago.edu/doi/full/10.1086/588063)
Geyer, C. J. (2009). Likelihood Inference in Exponential Families and
Directions of Recession. Electronic Journal of Statistics, 3, 259–289.
Wu, S., Shen, X., and Geyer, C. J. (2009). Adaptive Regularization using
the Entire Solution Surface. Biometrika, 96, 513–527.
Shaw, R. G. and Geyer, C. J. (2010). Inferring Fitness Landscapes. Evolution, 64, 2510-2520.
Okabayashi, S., Johnson, L. and Geyer, C. J. (2011). Extending PseudoLikelihood for Potts Models. Statistica Sinica, 21, 331–347.
Geyer, C. J. (2011). Introduction to MCMC. In Handbook of Markov
Chain Monte Carlo. Edited by S. P. Brooks, A. E. Gelman, G. L. Jones,
and X. L. Meng. Chapman & Hall/CRC, Boca Raton, pp. 3–48.
Geyer, C. J. (2011). Importance Sampling, Simulated Tempering, and
Umbrella Sampling. In Handbook of Markov Chain Monte Carlo. Edited
by S. P. Brooks, A. E. Gelman, G. L. Jones, and X. L. Meng. Chapman
& Hall/CRC, Boca Raton, pp. 295–311.
Tewari, S., Geyer, C. J. and Mohan, N. (2011). A Statistical Model for
Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets. IEEE Transactions on Power Systems, 29,
2031–2039.
De Andrade, B. B. and Geyer, C. J. (2011). Nonstandard Central Limit
Theorems for Markov Chains. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 19, 251–274.
Okabayashi, S. and Geyer, C. J. (2012). Gradient-based Search for Maximum Likelihood in Exponential Families. Electronic Journal of Statistics,
6, 123–147.
Johnson, L. T. and Geyer, C. J. (2012). Variable Transformation to Obtain Geometric Ergodicity in the Random-walk Metropolis Algorithm.
Annals of Statistics, 40, 3050–3076. Correction: (2013) Annals of Statistics, 41, 2698.
Geyer, C. J. and Meeden, G. D. (2013). Asymptotics for Constrained
Dirichlet Distributions. Bayesian Analysis, 8, 89–110.
Geyer, C. J. (2013). Asymptotics of Maximum Likelihood without the
LLN or CLT or Sample Size Going to Infinity. In Advances in Modern
Statistical Theory and Applications: A Festschrift in honor of Morris L.
Eaton, G. L. Jones and X. Shen eds. IMS Collections, Vol. 10, pp. 1–24.
Institute of Mathematical Statistics: Hayward, CA.
Geyer, C. J., Ridley, C. E., Latta, R. G., Etterson, J. R and Shaw, R. G.
(2013). Local Adaptation and Genetic Effects on Fitness: Calculations
for Exponential Family Models with Random Effects. Annals of Applied
Statistics, 7, 1778–1795.
Price, B. S., Geyer, C. J., and Rothman, A. J. (2015). Ridge Fusion in
Statistical Learning. Journal of Computational and Graphical Statistics,
24, 439–454.
Shaw, R. G., Wagenius, S., and Geyer, C. J. (2015). The susceptibility of
Echinacea angustifolia to a specialist aphid: eco-evolutionary perspective
on genotypic variation and demographic consequences. Journal of Ecology,
103, 809–818.
Eck, D., Shaw, R. G., Geyer, C. J., and Kingsolver, J. (2015). An integrated analysis of phenotypic selection on insect body size and development time. Evolution, 69, 2525–2532.
Geyer, C. J. and de Andrade, B. B. (2015). Near equivalence on metric spaces and a nonstandard central limit theorem. Journal of Logic &
Analysis, 7, 1–20.
Submitted:
Geyer, C. J. and Meeden, G. D. Bayesian asymptotics with locally asymptotically normal likelihood and discontinuous priors. Submitted to Bayesian
Analysis.
In Revision:
Geyer, C. J. Fuzzy P -values and ties in nonparametric tests. http://
www.stat.umn.edu/geyer/fuzz/geytie.html.
Eck, D. J., Geyer, C. J., and Cook, R. D. Enveloping the aster model.
Technical Reports and Conference Proceedings:
Geyer, C. J. (1988). Software for calculating gene survival and multigene
descent probabilities and for pedigree manipulation and drawing. Technical Report 153, Department of Statistics, University of Washington.
Geyer, C. J. and Thompson, E. A. (1990). Three papers on maximum
likelihood in exponential families. Technical Report 188, Department of
Statistics, University of Washington.
Geyer, C. J. (1991). Markov chain Monte Carlo maximum likelihood.
Computing Science and Statistics: Proc. 23rd Symp. Interface, 156–163.
http://purl.umn.edu/58440.
Geyer, C. J. (1991). Estimating Normalizing Constants and Reweighting
Mixtures in Markov Chain Monte Carlo. Technical Report No. 568. School
of Statistics, University of Minnesota. http://purl.umn.edu/58433.
Geyer, C. J. and Thompson, E. A. (1995). A new approach to the joint
estimation of relationship from DNA fingerprint data. In Population Management for Survival and Recovery: Analytical Methods and Strategies in
Small Population Conservation. eds. J. D. Ballou, M. Gilpin, T. J. Foose,
245–260. New York: Columbia University Press.
Avise J. C., Haig, S. M., Ryder, O. A., Lynch, M., and Geyer, C. J. (1995).
Descriptive genetic studies: applications in population management and
conservation biology. In Population Management for Survival and Recovery: Analytical Methods and Strategies in Small Population Conservation.
eds. J. D. Ballou, M. Gilpin, T. J. Foose, New York: Columbia University
Press, 183–244.
Mira, A. and Geyer, C. J. (2000). On non-reversible Markov chains. In
Madras, N. (ed.) Monte Carlo Methods Fields Institute Communications,
Fields Institute, Toronto, Canada.
Geyer, C. J. (2005). Le Cam Made Simple: Asymptotics of Maximum
Likelihood without the LLN or CLT or Sample Size Going to Infinity.
Technical Report No. 643 (revised). School of Statistics, University of
Minnesota. http://www.stat.umn.edu/geyer/lecam/.
Thompson, E. A. and Geyer, C. J. (2005). Fuzzy P-values in Latent
Variable Problems. Technical Report No. 481. Department of Statistics, University of Washington. http://www.stat.umn.edu/geyer/fuzz/
geytho.html.
Geyer, C. J., Wagenius, S., and Shaw, R. G. (2005). Aster Models for
Life History Analysis. Technical Report No. 644. School of Statistics,
University of Minnesota. http://www.stat.umn.edu/geyer/aster/.
Geyer, C. J., Lazar, R. C., and Meeden, G. D. (2005). Computing the
Joint Range of a Set of Expections. Proceedings of the Fourth International
Symposium on Imprecise Probabilities and their Applications. http://
www.sipta.org/isipta05/proceedings/papers/s063.pdf.
Geyer, C. J. (2006). Correlated Child Nodes in Aster Models Technical
Report No. 653. School of Statistics, University of Minnesota. http:
//www.stat.umn.edu/geyer/aster/.
Geyer, C. J. (2007). Radically Elementary Probability and Statistics.
Technical Report No. 657. School of Statistics, University of Minnesota.
http://www.stat.umn.edu/geyer/nsa/.
Shaw, R. G., Geyer, C. J., Wagenius, S., Hangelbroek, H. H., and Etterson, J. R. (2007). Supporting Data Analysis for “Unifying Life History Analysis for Inference of Fitness and Population Growth”. Technical
Report No. 658. School of Statistics, University of Minnesota. http:
//www.stat.umn.edu/geyer/aster/.
Shaw, R. G., Geyer, C. J., Wagenius, S., Hangelbroek, H. H., and Etterson, J. R. (2007). More Supporting Data Analysis for “Unifying Life
History Analysis for Inference of Fitness and Population Growth”. Technical Report No. 661. School of Statistics, University of Minnesota. http:
//www.stat.umn.edu/geyer/aster/.
Shaw, R. G., Geyer, C. J., Wagenius, S., Hangelbroek, H. H., and Etterson, J. R. (2008). Yet More Supporting Data Analysis for “Unifying
Life History Analysis for Inference of Fitness and Population Growth”.
Technical Report No. 666. School of Statistics, University of Minnesota.
http://www.stat.umn.edu/geyer/aster/.
Geyer, C. J., and Shaw, R. G. (2008). Supporting Data Analysis for a
talk to be given at Evolution 2008. Technical Report No. 669. School of
Statistics, University of Minnesota. http://purl.umn.edu/56204.
Geyer, C. J., and Shaw, R. G. (2008). Commentary on Lande-Arnold
Analysis. Technical Report No. 670. School of Statistics, University of
Minnesota. http://purl.umn.edu/56218.
Geyer, C. J., and Shaw, R. G. (2009). Model Selection in Estimation
of Fitness Landscapes. Technical Report No. 671 (revised). School of
Statistics, University of Minnesota. http://purl.umn.edu/56219.
Geyer, C. J. (2008) Supporting Theory and Data Analysis for “Likelihood
Inference in Exponential Families and Directions of Recession” Technical
Report No. 672. School of Statistics, University of Minnesota. http:
//www.stat.umn.edu/geyer/gdor/.
Geyer, C. J. (2009) More Supporting Data Analysis for “Likelihood Inference in Exponential Families and Directions of Recession” Technical
Report No. 673. School of Statistics, University of Minnesota. http:
//www.stat.umn.edu/geyer/gdor/.
Geyer, C. J., and Shaw, R. G. (2010). Hypothesis Tests and Confidence
Intervals Involving Fitness Landscapes fit by Aster Models. Technical
Report No. 674 (revised). School of Statistics, University of Minnesota.
http://purl.umn.edu/56328.
Geyer, C. J., and Shaw, R. G. (2010). Aster Models and Lande-Arnold
Beta. Technical Report No. 675 (revised). School of Statistics, University
of Minnesota. http://purl.umn.edu/56394.
Geyer, C. J. (2010). A Philosophical Look at Aster Models. Technical
Report No. 676. School of Statistics, University of Minnesota. http:
//purl.umn.edu/57163.
Geyer, C. J. (2010). Computation for the Introduction to MCMC Chapter
of Handbook of Markov Chain Monte Carlo. Technical Report No. 679.
School of Statistics, University of Minnesota. http://purl.umn.edu/
92549.
Johnson, L. and Geyer, C. J. (2011). Geometric Ergodicity of a RandomWalk Metropolis Algorithm for a Transformed Density. Technical Report
No. 680. School of Statistics, University of Minnesota. http://purl.
umn.edu/96959.
Geyer, C. J., Ridley, C. E., Latta, R. G., Etterson, J. R and Shaw, R. G.
(2012). Aster Models with Random Effects via Penalized Likelihood.
Technical Report No. 692. School of Statistics, University of Minnesota.
http://purl.umn.edu/135870.
Geyer, C. J., and Shaw, R. G. (2013). Aster Models with Random Effects
and Additive Genetic Variance for Fitness. Technical Report No. 696.
School of Statistics, University of Minnesota. http://purl.umn.edu/
152355.
Eck, D., Shaw, R. G., Geyer, C. J., and Kingsolver, J. (2015). Supporting
Data Analysis for “An Integrated Analysis of Phenotypic Selection on
Insect Body Size and Development Time”. Technical Report No. 698
(revised). School of Statistics, University of Minnesota. http://hdl.
handle.net/11299/172272.
R Packages on CRAN:
Geyer, C. J. (2015). R package aster (Aster Models). Current version 0.831 (2015-07-17). http://www.stat.umn.edu/geyer/aster/ and http:
//cran.r-project.org/package=aster
Geyer, C. J. (2015). R package aster2 (Aster Models). Current version 0.2-1 (2015-05-11). http://www.stat.umn.edu/geyer/aster/ and
http://cran.r-project.org/package=aster2
Geyer, C. J. (2015). R package fuzzyRankTests (Fuzzy Rank Tests
and Confidence Intervals). Current version 0.3-7 (2015-07-07). http:
//www.stat.umn.edu/geyer/fuzz/ and http://cran.r-project.org/
package=fuzzyRankTests
Geyer, C. J. and Johnson, L. T. (2015). R package mcmc (Markov Chain
Monte Carlo). Current version 0.9-4 (2015-07-17). http://www.stat.
umn.edu/geyer/mcmc/ and http://cran.r-project.org/package=mcmc
Geyer, C. J. (2013). R package nice (Get or Set UNIX Niceness). Current
version 0.4 (2013-03-31). http://www.stat.umn.edu/geyer/nice/ and
http://cran.r-project.org/package=nice
Meeden, G., Lazar, R., and Geyer, C. J. (2015). R package polyapost
(Simulating from the Polya Posterior). Current version 1.4-2 (2015-07-08).
http://cran.r-project.org/package=polyapost
Geyer, C. J. (2015). R package pooh (Partial Orders and Relations).
Current version 0.3-1 (2015-07-04). http://www.stat.umn.edu/geyer/
pooh/ and http://cran.r-project.org/package=pooh
Geyer, C. J. and Johnson, L. (2015). R package potts (Potts Models).
Current version 0.5-4 (2015-07-05). http://www.stat.umn.edu/geyer/
mcmc/ and http://cran.r-project.org/package=potts
Geyer, C. J., Meeden, G. D., and Fukuda, K. (2015). R package rcdd
(C Double Description for R). Current version 1.1-9 (2015-07-06). http:
//www.stat.umn.edu/geyer/rcdd/ and http://cran.r-project.org/
package=rcdd
Geyer, C. J. (2015). R package trust (Trust Region Optimization).
Current version 0.1-7 (2015-07-04). http://www.stat.umn.edu/geyer/
trust/ and http://cran.r-project.org/package=trust
Sheng, J., Qiu, P., and Geyer, C. J. (2013). R package TSHRC (Two Stage
Hazard Rate Comparison). Current version 0.1-3 (2013-03-30). http:
//cran.r-project.org/package=TSHRC
Geyer, C. J. and Meeden, G. D. (2015). R package ump (Uniformly Most
Powerful Tests). Current version 0.5-6 (2015-07-03). http://www.stat.
umn.edu/geyer/fuzz/ and http://cran.r-project.org/package=ump
R Packages on Github:
Geyer, C. J. (2015). R package bar (Demo Regression Models). Current
version 0.2-2 (2015-07-01). https://github.com/cjgeyer/bar
Geyer, C. J. (2015). R package baz (Demo Calling BLAS or LAPACK
from C Called from R). Current version 0.2-2 (2015-07-01). https://
github.com/cjgeyer/mat
Geyer, C. J. and Sung, Y. J. (2014). R package bernor (Bernoulli Regression with Normal Random Effects). Current version 0.3-9 (2014-05-20).
https://github.com/cjgeyer/bernor
Geyer, C. J. (2015). R package foo (Demo calling C or Fortran from R).
Current version 0.4-2 (2015-07-01). https://github.com/cjgeyer/foo
Geyer, C. J. (2015). R package qux (Demo Calling R from C or Fortran
Called from R). Current version 0.2-2 (2015-07-01). https://github.
com/cjgeyer/qux